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AI in manufacturing can help bring the economy back on its feet

During the coronavirus outbreak, practicing social distancing and implementing lockdowns are the only viable solution to mitigate the virus’s spread. But, due to this, many industries are suffering, and world economies are facing trying times.

Reports suggest that the pandemic could cause the global economy to shrink by 1% in 2020. The statistic makes sense because the coronavirus has disrupted global supply chains and halted international trade activities. Industries such as hospitality and tourism have taken the biggest hit since world tourism is practically impossible during a pandemic. Large-scale events that cater to large gatherings (such as music festivals and movie screenings) have been postponed indefinitely. And, unless we develop a vaccine or achieve herd immunity, there are no signs of such events returning anytime soon. 

So, while the present reality isn’t showing many signs of economic promise, we don’t have any other choice but to put on our problem-solving hats and tackle this challenge. Emerging technologies such as AI can provide a much-needed boost to the economy. There are many challenges to confront due to the coronavirus pandemic. The one I’d like to focus on is the hurdles in manufacturing.

Problems faced by manufacturing

Manufacturing by nature doesn’t provide the luxury of working remotely, unlike industries such as Information Technology. And, the production of articles on a large scale using machinery cannot stop due to a pandemic. Most manufacturing setups are in developing countries, and halting operations will displace many workers and challenge their livelihoods. It will also create worldwide shortages which can cause further crises.

AI can get the manufacturing economy back on track

It’s clear that out of all industries affected, operations within manufacturing needs to return to normalcy as soon as possible. This can take place only if we can provide a hygienic and safe working environment that doesn’t put workers at risk of infection. Creating low contact processes and automating various factory floors could be strong first steps to get manufacturing up and running, and here’s where AI could provide saving grace, thus contributing to the economy:

AI in manufacturing

During times like these, AI can help manufacturing units by offering higher quality control, higher hygiene levels for workers, and transparency across teams with better communication. Also, AI-inspired robots can allow workers to maintain safe distances from each other, thus lowering the odds of contracting the virus. AI tools can give manufacturing the advantage it needs during this health crisis, and here’s how it will help get the economy back on track:

Contact-less machine control

Ideally, manufacturing floors will want to reduce the number of times workers come in contact with objects and shopfloor equipment pieces. AI-inspired gesture identifiers could take advantage of the human voice or human gestures. This is handy for switching on motors, initiating assembly line processes, and so on. For example, in a glass factory, workers could increase a furnace’s temperature by simply voicing a command or performing a hand gesture. Factories can train workers to use such new generation techniques and equipment. This will increase their skills and also reduce their chances of getting infected.

AI offers contact-less solutions for controlling machines

To develop such AI systems, we will need a variety of training data. Data that can teach systems to understand human communication and human languages. Such data will then be converted into system signals that instruct various mechanical processes. If it’s a machine that recognizes speech commands, the training data will comprise a large volume of audio files. Files that represent instructions, commands, and orders. And, if it’s meant to recognize hand gestures, it would use multiple annotated images of various hand signs that indicate a certain instruction or command. 

If implemented, this could allow workers to avoid touching surfaces and thus encourage a safe working environment.

Intelligent Automation

AI can automate many processes in factory assembly lines. Processes that presently use many workers at once. Automated processes will help factories stop workers from crowding in a single space. 

For example, factories need to review and screen their products before shipment. The reviewing and screening process is generally a manual one, in which multiple workers physically examine the same product. This increases the number of contact points, thus increasing the probability of infection spread. 

Automation will bring manufacturing back to normalcy

With the help of Artificial Intelligence, such review processes can be automated. AI developers can train computer vision systems to identify faulty products and packaging issues, after which they can alert concerned officers. Computer vision systems can also monitor the manufacturing floor. This will be especially useful for observing chemical reactions, heating/cooling operations, cutting/joining processes, and so on.

Leveraging data to handle supply/demand

Due to the coronavirus outbreak, manufacturing units cannot depend on historical figures to predict supply and demand. Demand for manufactured products has dwindled across industries. And, due to the world’s supply chain taking a hit, businesses can’t manufacture products with pre-pandemic efficiency.

AI can use manufacturing data to predict supply and demand

Here’s where businesses can leverage the power of data. Artificial Intelligence models can learn from present trends in supply and demand, to suggest manufacturing solutions. For example, an AI model can scan databases representing the demand for a certain product and determine how much of that product a factory should manufacture. Such models can also study the amount of raw material generated across the world. This helps determine whether a factory will be able to meet present-day demands.

Using such data, businesses can zero-in on target markets and ensure that their inventory allows them to cater to such markets. 

Conclusion

The coronavirus outbreak has revealed how Artificial Intelligence, Machine Learning, and Computer Vision are a blessing in disguise. Using such emerging technology, businesses can figure out novel methods to sail through these new and economically terrifying circumstances. Just like in manufacturing, we can develop AI systems for various industrial processes to ensure minimized contact without compromising on efficiency. With technology such as AI, we might just be able to maneuver through the pandemic successfully and bring the economy back on its feet.

Mistakes to avoid while training AI models

Artificial Intelligence involves the pursuit of human-ness in technology. Like teaching a child, AI development involves two things. The first being providing the study material (training data) and second being the learning method (Machine Learning, Deep Learning, etc.). 

For an ML model to perform well, it requires extensive training with a variety of training data. ML models consuming large amounts of training data allows them to understand diverse examples. And, a comprehensive training process increases the model’s odds of understanding and acting on the data at hand. 

The common problem faced by most developers is a misapplication of what was mentioned above. Simple strategy based problems have quick fixes, but those can seem distant or non-existent during the thick of the development phase. Here are some of the common mistakes developers make while training AI models, along with tips to avoid them:

Poor training data development

Training data is the juice that keeps AI/ML models functioning. Bad quality training data leads to bad quality results. It’s as simple as that. Bad quality is a broad term here, so allow me to break it down:

Lack of training data

ML models need multiple examples of a situation to understand how to tackle it. When there is a lack of training data, your model will not be able to identify real-world examples effectively. Analogous to how we learn, an AI model can function as required only if there has been a large number of examples to learn from (in this case, a large amount of training data). 

Unclean data

Having a large volume of training data is worth nothing if it’s quality is below par. Training data that’s riddled with errors will only confuse your ML model, which will render it unusable. Think about it, you can’t expect a student to learn if the reading material is filled with mistakes. 

Common examples of unclean data include inaccurately annotated images and videos, irrelevant data points, faulty conversational datasets (generally poor grammar and tonal issues).

Narrow data

To add the element of human experience to your AI/ML model, developers need to train it to understand specific rare scenarios and edge cases. Many AI developers falter here. They build algorithmically sound models, but they don’t train it to perform well when encountered with uncommon scenarios. For example, if an autonomous vehicle isn’t trained to tackle rare situations (such as protestors on the street, kids randomly running, etc.), the end result could be fatal.

The straightforward but tedious solution to solving this is exploring all scenarios your model might encounter, and feed datasets that represent all possible circumstances.

AI/ML model development snags

Even if the training data is sound, the AI/ML model at hand needs to be powerful enough to not only consume that data but reproduce usable results. Here are some common mistakes:

Machine Learning where it isn’t necessary

Yes, in many scenarios, companies decide to implement machine learning even when it doesn’t serve the purpose or serves it inadequately. In many situations, procedural logic does the job, so determine the need for ML implementation accordingly.

Performance analysis

Even if an ML model can perform the right processes with the data fed to it, there might be issues beyond training data and AI/ML algorithms that can restrict the model from functioning effectively. Consider this performance-related issue: if the model exhibits a lag while producing results, that might not help in certain use cases. Taking the example of an autonomous vehicle, if it takes even as long as a second to identify a pedestrian in the middle of a street, the vehicle might still end up causing an accident. Factors surrounding performance influence real-life consequences, so it’s important to identify such issues.

Mixing up correlation and causation

It’s easy to allow your ML model to function based on correlating certain data points consumed to determine a cause. Consider this conflation of correlation and causation: “The faster windmills rotate, the more wind is observed. Hence wind is caused by rotation.”

While that statement’s fault might seem obvious to us, it might be fair logic to an AI/ML model’s mind. In most cases, acting based on correlation may not have significant adverse consequences. But, it displays an inaccuracy in the model’s algorithm. Ideally, correlation and causation shouldn’t be misunderstood, even by an AI/ML model.

Conclusion

Training an AI model is no simple feat. It involves a comprehensive understanding of the human mind and a serious attempt to replicate it. We’re making great strides in the science of Artificial Intelligence, but we still have ways to go. We can traverse those ways faster if we identify and eliminate key mistakes that make our model’s performance suffer. And we can do that only if we understand the common mistakes that we need to avoid while training and developing our AI models.

AI Trends: AI and Medicine

April 21, 2020 | Artificial Intelligence | No Comments

AI and Medicine

Artificial Intelligence is here to improve our lives, by not just making things more efficient, but also increasing our lifespan. Companies across industries are experiencing the advantages that come with AI innovation, especially the healthcare space. 

Throughout human history, we’ve been able to understand the parameters that determine health better, and we’ve developed accompanying technology. With vaccines in the late 1700s, anesthesia and medical imaging in the 1800s, to organ transplant and immunology in the 1900s, healthcare innovation has been on an upward slope. In the 21st century, AI in medicine is showing us how that slope can rise further. Here are some popular trends in AI and medicine:

Virtual Doctors

Chatbots have experienced a rise in popularity across industry operations, and healthcare has an important use case for them. In a time such as the coronavirus outbreak, there aren’t enough medical professionals available to attend to all patients. In the USA, the ratio of physicians to the country’s population is around 277 to 100,000. The World Health Organization recommends a 1:1000 ratio, but 44% of its member nations don’t meet this criterion.

Conversational AI in medicine

Here’s where medical chatbots can reduce that gap. Chatbots offer 24/7 accessibility and instant responses. For patients facing unusual symptoms, they can address their health issues from the comfort of their homes with a chatbot. Conversational AI in medicine can be trained with conversational datasets that represent diagnoses for various diseases. Accordingly, the chatbot can recommend direct solutions for simpler cases or schedule a doctor’s appointment for the more serious ones. 

Treating Patients With Alzheimer

Many of our populations’ senior citizens end up suffering from Alzheimer’s disease. It’s the first step towards severe cognitive decline and it can have a toll on the affected individual and his/her loved ones. While it has always been hard to predict the chances of cognitive decline due to Alzheimer’s, today, AI can reliably perform the same prediction. 

Using a combination of biometric data and cognitive tests, AI models can determine a patient’s risk levels. Also, AI models are learning how to convert brain signals into machine-readable text. Such technology can also improve Alzheimer’s research and therapy.

Vaccine Identification

The coronavirus pandemic has created the wild chase for a vaccine, for its the solution to ending the world’s present lockdown. Organizations across industries are exploring ways in which they can contribute. This has led to exciting players entering the vaccine game. Tech giants such as Microsoft and Google are developing AI solutions for vaccine development. 

AI in vaccine development

When it comes to viral infections, their vaccines are produced by combining the original virus with another virus that can weaken the former. This combination will ensure that the virus to be tackled cannot reproduce or multiply well, thus being rendered ineffective. 

But, vaccine development is easier said than done. Pathogens have highly complex methods for dodging mainstream medication. Pathologists have to go through multiple protein structures and medicinal ingredients to zero in on the one that will cripple the virus or bacteria at hand.

Here’s where AI comes in. Artificial Intelligence models can read through thousands of research papers within the same time it takes a researcher to go through about 10. Also, an AI model can recommend vaccine ingredients that exhibit higher chances of success. Training such a model involves making it understand how protein folding and virus-to-virus interaction takes place. With the appropriate training datasets, AI models will be ready to help medical researchers and pathologists locate the right vaccine. Since clinical trials always are the rate-limiting step, AI models provide the advantage of saving time in other areas of vaccine development.

Managing Medical Records

Data is everywhere, and medical records have plenty of that. These records contain patients’ medical history, diseases, surgeries, etc. They also include treatment methodologies used for each scenario. So now, we’ve got a database of patients’ health issues with its accompanying solution. Such data can be converted into machine-readable text for AI models to consume. Once these models receive a list of a patient’s symptoms, using the data consumed, they can perform simple diagnoses and suggest treatment methods and medication. This can help doctors save time and be on top of their game even when presented with rare cases.

Wearables

Popular wearables such as Fitbit Flex, Samsung Gear 2, and Apple Watch have revolutionized the way we communicate with our mobile devices. It has also shown us how it can aid our fitness regimes, with counting our steps and measuring our heart rates. But, wearable tech has potential beyond the above-mentioned activities. Advanced wearables can today detect quivering or irregular heartbeats, symptoms that are a sign of possible blood clots, stroke, heart failure, and other complications concerning the heart. Such information can be sent directly to a medical professional for immediate attention.

Cancer Treatment

AI in cancer therapy has allowed for numerous solutions to tackling this fatal disease. Computer vision systems can be trained to detect cancerous cells and understand what these cells look like at different stages. Adopting computer vision into cancer diagnosis results brings higher accuracy into the picture. This helping doctors and medical professionals provide better treatment options and extend more lives.

Surgery

Surgical procedures require high-level precision, and it takes a skilled hand to perform them successfully. AI-inspired surgical equipment could help guide a surgeon performing a complex procedure such as open-heart surgery, cesarean section, cataract, etc. 

AI in surgery

Using a combination of visual datasets and movement-based training, surgical AI equipment can locate parts of the body that need attention, sometimes better than a surgeon can. When developed successfully, such technology can revolutionize surgical procedures by increasing the number of successes and increasing patients’ trust.

AI-inspired genomic medicine

The National Human Genome Research Institute describes genomic medicine as a medical discipline that involves using genomic information about an individual for clinical care. Already popular in oncology and infectious diseases, this emerging field of medicine has arrived during the same era as Artificial Intelligence. By consuming large volumes of datasets containing genomic information, AI models can learn how to detect patterns and provide first draft health reports. Doctors can review such reports and use them for their medical practice.

Conclusion

While the avenues for AI implementation in healthcare are practically endless, AI’s ability to provide accurate results becomes the defining factor. Healthcare involves life or death situations, and any room for error could result in undesirable consequences.

Human error is inevitable and it’s something even the world’s best medical professionals can be victims of. Here’s where AI can relieve them of some pressure. AI models in medicine can learn from well-built training data sets, identify trends, and provide recommendations for medical practitioners.

Especially during this coronavirus outbreak, we need to provide doctors with the best technology available for them to perform their tasks with higher precision. AI trends provide for good news at this point, and hopefully, it’s forward sloping curve flattens out the coronavirus’.

AI and COVID 19

As we experience the coronavirus pandemic, we have an influential role to play for mitigating the virus’s spread. At an individual level, social distancing goes a long way in blocking potential routes for infection spread. Added to that, if you wash your hands regularly and wear a mask whenever you’re in public, you’re doing your part to keep the coronavirus at bay!

COVID 19 - the coronavirus

Businesses also have a vital role to play here, and they’re exploring avenues to help fight this battle. Essential services are operating around the clock to ensure their inventories are full. Governments have also roped in private firms to help with medical resources. For example, the Trump Administration issued an order under the Defense Production Act to make General Motors manufacture surgical masks at scale. 

Along with these measures, companies that have a working knowledge of Artificial Intelligence are identifying coronavirus-relevant use cases and developing intelligent models. As an emerging technology, industries are slowly adopting AI. With the hopes of mitigating the spread or finding a cure to COVID-19, here’s how AI can be used for tackling it:

Vaccine development

Historically, vaccine development has been an effective strategy for tackling contagious diseases. This is why pathologists are researching numerous relevant antigens and immunogens for tackling COVID 19. The AI strategy for vaccine identification is two-fold: 

  • Suggesting vaccine components by understanding viral protein structures
  • Scouring through medical research papers at a pace faster than medical professionals

Pathologists are researching nucleic acid vaccines (one of the three types of vaccines, others being whole-pathogen vaccines and subunit vaccines) as these are the ones relevant to crippling the coronavirus. AI helps pathologists understand a variety of molecule structures, and their ability to fold the coronavirus (the action that will render the virus ineffective).

COVID 19 vaccine development

Korea-based Deargen created a deep-learning model that can employ simplified chemical sequences that can predict a molecule’s chances of disabling the coronavirus. Google’s DeepMind is using Artificial Intelligence models to study properties of the novel virus, which includes understanding the protein structures associated with SARS-CoV-2. 

Chatbots for diagnoses 

Chatbots help companies save time and resources. As the coronavirus spreads, the world has understood that hospitals are overloaded. And, most countries don’t have the right medical infrastructure to handle numerous patients.

AI in diagnosis using chatbots

Patients can use medical chatbots to perform quick diagnoses. after which it can predict the likelihood of a patient being infected. This ensures that only patients who have a high probability of testing positive end up visiting a hospital. Medical chatbots reduce the load on medical professionals and hospital resources, thus saving lives truly at risk.

Monitoring infection spread

With location data, we can identify infection hotspots across geographies. This can be used to predict the odds of infection spread based on earlier instances. Users report whether they’re positive or negative on an app. And based on that user data, the app can determine which parts of a neighborhood need to be avoided. It can also notify users who have visited hotspots and request them to get tested. Such data will be immensely valuable to governments imposing lockdowns, and it will provide them with the data needed for creating a path to normalcy.

COVID 19 infection spread

South Korea used an app that can locate infected citizens and their local travel history. During a mandatory two-week self-quarantine, the app helped the South Korean government trace infection spread by identifying cases wherein people were at a two meters distance from each other. Using this app helped South Korea flatten the curve, thus exhibiting how this battle is one that can be won. 

Israel has created a survey reading AI that takes advantage of user-reported data for tackling the COVID 19 spread. Using this, populations can be warned about risky locations, and users at a higher risk of contamination can avoid infection. 

Conclusion

The world is now on red alert and everyone is battling this pandemic together. Social distancing has proven to be the single most effective tool we have at our disposal. People have been asked (in many cases forced by law) to wear masks and maintain physical distance while in public. For the ones already infected, hospitals are doing their best to ensure the right resources such as hospital beds and ventilators are available.

While all this is good, we still have the problem of a shortage of medical professionals, nurses, and hospital resources. Also, we don’t fully understand this virus and a vaccine isn’t exactly close to being ready for usage. Here’s where we’ve got Artificial Intelligence at our disposal. 

AI can speed up human processes and allow us to quickly reach our process’ finish line. By using AI for vaccine creation, pathologists can locate and develop a suitable vaccine faster for tackling COVID 19. Chatbots can reduce the load on doctors and ensure multiple patients are tested. And, a combination of location and user data can help the public and governments understand what the situation surrounding infection spread looks like.

Coupled with human intelligence, AI has the potential to get us out of this pandemic sooner rather than later. The answer lies in the efficiency of AI implementation.